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Context: Data visualizations and dashboards are essential in disseminating information to broad audiences. However, designing these instruments is complex due to the multitude of involved factors. Theoretical frameworks are therefore critical to guide the design and implementation of data visualizations. Objective: This article presents the approach used to pinpoint key features that impact the creation of information visualizations, organize these features using meta‐modelling, and validate the final artefact through expert judgement. Method: Meta‐modelling facilitates the distillation of characteristics and functionalities critical for the development of information systems. Utilizing this method, a dashboard meta‐model was constructed in preceding research, yielding a framework of abstract components and connections to delineate specific products. This meta‐model serves as a robust tool for the automated generation of information visualizations and dashboards. Nonetheless, to confirm its quality and efficacy, it underwent a thorough quality assessment and expert evaluation to identify and address potential shortcomings. Results: The validation process resulted in positive ratings and constructive feedback to enhance the meta‐model's present iteration. Experts mainly highlighted the meta‐model's complexity and the clarity of certain entities, noting potential confusion for non‐experts. Conclusions: While the meta‐model is confirmed to be a coherent and pertinent tool, it requires additional clarifications to simplify its complexity. Addressing this, graphical interfaces have been developed to enable users to interact with the dashboard meta‐model—and to create dashboard instances—through straightforward interactions. [ABSTRACT FROM AUTHOR]

Background: Treatment optimization is mandatory in psychiatric diseases and the use of population pharmacokinetics (popPK) models through model informed precision dosing (MIPD) has the potential to improve patient medical care. In this perspective, meta-modelling methods could provide popPK models with improved predictive performances and most of covariates of interest. The aims of this study were to develop meta-models of clozapine and lithium, assess their predictability and propose optimized dosing regimens for both drugs. Methods: Two popPK models for each drug were retained to develop the meta-models. For clozapine, the model with the best predictive performances and gender as a covariate and one with smoking status were retained. For lithium, the model with the best predictive performances and fat-free mass as covariate and one with glomerular filtration rate were retained. Results: Both meta-models showed improved predictability compared to the original models. Clozapine meta-model simulations allowed us to propose dosing regimen according to gender and smoking status. Steady-state doses ranged from 375 to 725 mg/day for clozapine once daily, and from 350 to 650 mg/day for clozapine twice daily. Lithium meta-model simulations allowed us to propose dosing regimen according to weight, body mass index, gender and GFR. Our steady-state dose propositions ranged from 625 to 1125 mg/day for males, and from 375 to 750 mg/day for females. Conclusion: Both meta-models met the acceptability criteria for use in clinical practice on all subpopulations of interest. Those models could be used in the perspective of MIPD for clozapine and lithium. [ABSTRACT FROM AUTHOR]

The use of Agent-Based Models (ABMs) to make predictions in real-time is hindered by their high computation cost and the lack of detailed individual data. This paper proposes a new framework to enable the use of emulators, also referred to as surrogate models or meta-models, coupled with ABMs, to allow for real-time predictions of the behaviour of a complex system. The case study is that of pedestrian movements through an environment. We evaluate two different types of emulators: a regression emulator based on a Random Forest and a time-series emulator using a Long Short-Term Memory neural network. Both emulators perform well, but the time-series emulator proves to generalise better to cases where the number of agents in the system is not known a priori. The results have implications for the real-time modelling of human crowds, suggesting that emulation is a feasible approach to modelling crowds in real-time, where computational complexity prohibits the use of an ABM directly. [ABSTRACT FROM AUTHOR]

This paper looks at the tension between the desire to claim predictive ability for Agent-Based Models (ABMs) and its extreme difficulty for social and ecological systems, suggesting that this is the main cause for the continuance of a rhetoric of prediction that is at odds with what is achievable. Following others, it recommends that it is better to avoid giving the impression of predictive ability until this has been iteratively and independently verified, due to the danger of suggesting more than is empirically warranted, especially in non-modellers. It notes that there is a restricted and technical context where prediction is useful, that of meta-modelling – when we are trying to explain and understand our own simulation models. If one is going to claim prediction, then a lot more care needs to be taken, implying minimal standards in practice and transparent honesty about the empirical track record – the over-enthusiastic claiming of prediction in casual ways needs to cease. [ABSTRACT FROM AUTHOR]

During the last 30 years, enterprise modelling has been recognised as an efficient tool to externalise the knowledge of companies in order to understand their operations, to analyse their running and to design new systems from several points of view: functions, processes, decisions, resources and information technology. This paper aims at describing the long evolution of enterprise modelling techniques as well as one of the future challenges of these techniques: the transformation of enterprise models. So, in a first part, the paper describes the evolution of enterprise modelling techniques from the divergence era to the convergence period. In a second time, the paper focuses on the recent advances in the use of enterprise models through model-driven approaches, interoperability problem-solving and simulation, all these advances having the same characteristic to use the transformation of enterprise models. [ABSTRACT FROM AUTHOR]

Experimental optimization of physical and biological processes is a difficult task. To address this, sequential surrogate models combined with search algorithms have been employed to solve nonlinear high-dimensional design problems with expensive objective function evaluations. In this article, a hybrid surrogate framework was built to learn the optimal parameters of a diverse set of simulated design problems meant to represent real-world physical and biological processes in both dimensionality and nonlinearity. The framework uses a hybrid radial basis function/genetic algorithm with dynamic coordinate search response, utilizing the strengths of both algorithms. The new hybrid method performs at least as well as its constituent algorithms in 19 of 20 high-dimensional test functions, making it a very practical surrogate framework for a wide variety of optimization design problems. Experiments also show that the hybrid framework can be improved even more when optimizing processes with simulated noise. [ABSTRACT FROM AUTHOR]

• A novel Ring Hoop Plane Strain Test (RHPST) enhancing the characterization of tubes' plastic behavior has been engineered. • A meta -modeling approach integrating RSM and FEM was developed for the optimization-based design of the RHPST. • 3D DIC technology was used to precisely probe the edge effects and homogeneity of strain fields in RHPST sample. • A tailored correction was developed, improving the plane strain zone extent covering about 78% of the RHPST specimen's width. • Innovative grooved ring specimen was produced, improving plane strain zone extent up to 90% of the gauge width. The accurate characterization of the plastic behavior of tubular materials is challenging due to the difficulties in acquiring proper specimens for mechanical testing. In this study, we propose a novel testing technique, namely the ring hoop plane strain Test (RHPST), as a supplementary test for characterizing materials experiencing distinct strain paths compared to standard uniaxial tensile tests. To design a suitable ring specimen's geometry that satisfies the necessary plane strain conditions, we developed a meta -modelling approach merging response surface method (RSM) and the finite element method (FEM). Experimental tests are conducted on RHPST specimens, and 3D digital image correlation (DIC) technique is used to monitor the strain fields within the specimen's gauge section. However, observations indicate the presence of edge effects on the gauge specimen. These edge effects significantly influence and limit the homogeneity of the plane strain field. To tackle this limitation, a correction procedure is developed for separating the authentic plane strain area, constituting approximately 78% of the RHPST specimen's width. Furthermore, we successfully fabricated a novel grooved ring specimen (GRHPST), which exhibits a wider region of the plane strain state encompassing up to 90% of the gauge width, while minimizing the impact of edge effects. The true stress-true strain curve obtained from the GRHPST specimen exhibits exceptional agreement with the curve predicted using the Hill48 yield criterion. Notably, no additional corrections are required for the measured true stress-true strain curve, thereby affirming the efficacy of the GRHPST specimen as an adept test for characterizing the anisotropic plastic behavior of AA6063 tubes. [ABSTRACT FROM AUTHOR]

The effect of seventeen parameters (material, design and operating) on the efficiency of the long-term operation of borehole thermal energy storage (BTES) has been investigated in this paper. Global sensitivity analysis was used to determine this effect and the use of the fast meta-model to predict the thermal efficiency of BTES has also been proposed. Carrying out research on the real object using this many input parameters is impossible, therefore, the use of 3D transient simulation studies of heat and mass transport in the BTES was chosen. An efficient numerical technique, coupled with the design and analysis of the computational experiment method was used to significantly reduce the required calculation time. The results showed that the temperature of the inlet fluid during charging and discharging, as well as the thermal conductivity of the rock mass and the arrangement of the borehole heat exchangers, had a crucial impact on the efficiency of the BTES. In order to obtain high efficiency values for the BTES, it is necessary to produce the highest possible inlet fluid temperature during charging and the lowest possible temperature of the inlet fluid during discharging. The highest values of BTES efficiency can be achieved using a compact arrangement of the borehole heat exchangers (BHE spacing 1.5–3.5 m) and for low values of the thermal conductivity of the rock mass (less than 2.45 W/(m ⋅ K). Image 1 • The influence of 17 parameters on BTES efficiency was analysed. • The meta-model to predict BTES efficiency was proposed. • Four from seventeen parameters have a crucial impact on BTES efficiency. • 41% of the analysed BTES configurations achieved efficiency above 0.5. [ABSTRACT FROM AUTHOR]

Sheet-bulk metal forming (SBMF) is an emerging manufacturing technology that offers vast potential for lightweight design and functional integration. However, product designers often lack information about the required manufacturing parameters for their specific design. This problem is currently tackled by a knowledge acquisition process from manufacturing experts in combination with meta-modelling techniques. These meta-models predict resulting manufacturing parameters for given part design properties. When employing this design-for-manufacturing approach, it is key for product designers to evaluate many design alternatives that are all manufacturable with given forming machines. This is currently done by a genetic algorithm-based optimization of the meta-models with regard to manufacturing features. This papers proposes a novel approach for generating many design alternatives based on the meta-models and gathering them in a solution space. In doing so it is possible to review all manufacturable sheet-bulk metal forming part variants and let the product designers decide which part design fits best. The approach is applied to a locking teeth part, which highlights the usability of the new method. [ABSTRACT FROM AUTHOR]

Industrial systems are constantly subject to random events with inevitable uncertainties in production factors, especially in processing times. Due to this stochastic nature, selecting appropriate dispatching rules has become a major issue in practical problems. However, previous research implies that using one dispatching rule does not necessarily yield an optimal schedule. Therefore, a new algorithm is proposed based on computer simulation and artificial neural networks (ANNs) to select the optimal dispatching rule for each machine from a set of rules in order to minimise the makespan in stochastic job shop scheduling problems (SJSSPs). The algorithm contributes to the previous work on job shop scheduling in three significant ways: (1) to the best of our knowledge it is the first time that an approach based on computer simulation and ANNs is proposed to select dispatching rules; (2) non-identical dispatching rules are considered for machines under stochastic environment; and (3) the algorithm is capable of finding the optimal solution of SJSSPs since it evaluates all possible solutions. The performance of the proposed algorithm is compared with computer simulation methods by replicating comprehensive simulation experiments. Extensive computational results for job shops with five and six machines indicate the superiority of the new algorithm compared to previous studies in the literature. [ABSTRACT FROM AUTHOR]

Current rates of retrofitting residential buildings are inadequate for meeting goals set by governments to reduce emissions. Plans, laws, policies, programs, and regulations must be tailored, yet adaptive to changes in technology, and have a way to communicate the evidence behind them. Surrogate models allow for accurate predictions much quicker than traditional brute-force methods with only small amounts of error. This research presents a methodology for developing a surrogate model that takes advantage of ANN's ability to make accurate predictions. Modelling is supported through analysing the alignment of input and output interactions with building science knowledge through SHAP values. Surrogate models developed using this methodology were able to predict the total annual site energy of an archetype representing 21% of Toronto's single-family housing stock with an average of 6% error, equivalent to 2.7 GJ per year. This research is another example of how the increased interpretability of black-box models support further development of surrogate models to solve optimisation problems such as developing effective retrofitting plans and policy strategies to accelerate rates of retrofitting existing housing. [ABSTRACT FROM AUTHOR]

The wave of interest in data-centric applications has spawned a high variety of data models, making it extremely difficult to evaluate, integrate or access them in a uniform way. Moreover, many recent models are too specific to allow immediate comparison with the others and do not easily support incremental model design. In this paper, we introduce GSMM, a meta-model based on the use of a generic graph that can be instantiated to a concrete data model by simply providing values for a restricted set of parameters and some high-level constraints, themselves represented as graphs. In GSMM, the concept of data schema is replaced by that of constraint, which allows the designer to impose structural restrictions on data in a very flexible way. GSMM includes GSL, a graph-based language for expressing queries and constraints that besides being applicable to data represented in GSMM, in principle, can be specialised and used for existing models where no language was defined. We show some sample applications of GSMM for deriving and comparing classical data models like the relational model, plain XML data, XML Schema, and time-varying semistructured data. We also show how GSMM can represent more recent modelling proposals: the triple stores, the BigTable model and Neo4j, a graph-based model for NoSQL data. A prototype showing the potential of the approach is also described. [ABSTRACT FROM AUTHOR]

Model transformations are key elements of model-driven engineering, where they are used to automate the manipulation of models. However, they are typed with respect to concrete source and target meta-models, making their reuse for other (even similar) meta-models challenging. To improve this situation, we propose capturing the typing requirements for reusing a transformation with other meta-models by the notion of a typing requirements model (TRM). A TRM describes the prerequisites that amodel transformation imposes on the source and targetmeta-models to obtain a correct typing. The key observation is that any meta-model pair that satisfies the TRM is a valid reuse context for the transformation at hand. A TRM is made of two domain requirement models (DRMs) describing the requirements for the source and target meta-models, and a compatibility model expressing dependencies between them. We define a notion of refinement between DRMs and see meta-models as a special case of DRM. We provide a catalogue of valid refinements and describe how to automatically extract a TRM from an ATL transformation. The approach is supported by our tool TOTEM. We report on two experiments--based on transformations developed by third parties and meta-model mutation techniques--validating the correctness and completeness of our TRM extraction procedure and confirming the power of TRMs to encode variability and support flexible reuse. [ABSTRACT FROM AUTHOR]

In the automotive industry, high efforts are made to design high-efficiency, low-cost, requirement-optimized electric drives. Traditionally, time-consuming and expensive 3D finite element analyses are used to assess the effects of different design parameters from various engineering disciplines such as the electromagnetics, heat transfer, and mechanics. Often, to decrease the calculation efforts, scalable, physics-based reduced order models are used. For setting up and interpreting such reduced order models, simulation experts employ implicit knowledge by applying heuristic correction factors. This paper proposes a novel method to formalizing such implicit simulation knowledge using Gaussian Processes. Furthermore, the successful application of the method to a case study of electric motors is reported by validating the aforementioned correction factors for different motor designs. [ABSTRACT FROM AUTHOR]